Double Deep Q Network (DDQN).
Deep Reinforcement Learning combines classical reinforcement learning with neural network function approximation. More specifically, Double Q-Learning [1]} is combined with Deep Q-Learning [2]} to address the issue of overestimating \(Q\)  values.
A parametric form of the state-action value function of Eq. (REF ), blue\(Q(s,a; \theta )\) , with parameters \(\theta \)  is often represented using neural networks:
\(\begin{split}\theta _{t+1} = \theta _t + {blue}{\alpha }[{blue}{r}_{t+1} {blue}{+} \gamma {max}_{a^{^{\prime }}}Q(s_{{blue}{t+1}}, a{blue}{^{^{\prime }}}; \theta _t) - \\- Q(s_t, a_t; \theta _t)]\nabla _{\theta _t}Q(s_t, a_t; \theta _t)\end{split}\) 
